Abstract: With the acceleration of urbanization,the dynamic change of subway passenger flow and the perturbation caused by uncertainty will affect the quality of urban rail transit operation service in China.This study proposes a passenger flow data enhancement method based on generative adversarial network for networked rail transit operation,which generates a large amount of usable data with the same characteristics by using a small amount of original passenger flow data for data enhancement.On the basis of passenger flow data enhancement,we further study the accurate prediction method of rail transit operation posture based on spatio-temporal multidimensionality,and propose a passenger flow data prediction method based on long-short-term memory network,convolutional neural network,and graphical neural network,which can realize the accurate prediction of the passenger flow data of the rail transit in the temporal dimension and spatio-temporal dimension,respectively.The generation and prediction of short-time passenger flow data can effectively alleviate the pressure of passenger flow.Additionally,accurate passenger flow prediction provides a solid foundation for adjusting train operations,improves the quality of rail transit services,and offers theoretical support for future urban development planning.

Key words: Urban rail transit, Data augmentation, Generative adversarial networks, Passenger flow prediction, Neural networks